数据仓库与数据挖掘

任课教师

:

戴维迪 工作单位: 计算机学院 办公地点： 25 -B -607 时间： 每周五下午 3 ： 00—5 ： 00 联系电话: 13820652299

E-mail: [email protected]

授课：

4-10 周，星期二、五

上机：

第 11 周， 25-B-610 星期二、五下午 2 ： 30

April 24, 2020 Data Mining: Concepts and Techniques 1

Data Mining: Concepts and Techniques

— Chapter 1 — — Introduction —

April 24, 2020

Jiawei Han and Micheline Kamber Department of Computer Science University of Illinois at Urbana-Champaign www.cs.uiuc.edu/~hanj

©2006 Jiawei Han and Micheline Kamber. All rights reserved.

Data Mining: Concepts and Techniques 2

April 24, 2020 Data Mining: Concepts and Techniques 3

April 24, 2020 Data Mining: Concepts and Techniques 4

Chapter 1. Introduction

 Motivation: Why data mining?

 What is data mining?

 Data Mining: On what kind of data?

 Data mining functionality  Are All the “Discovered” Patterns Interesting?

 Classification of data mining systems  Major issues in data mining  Overview of the course  Supplementary Lecture Slides April 24, 2020 Data Mining: Concepts and Techniques 5

Why Data Mining?

   The Explosive Growth of Data: from terabytes to petabytes  Data collection and data availability  Automated data collection tools, database systems, Web, computerized society  Major sources of abundant data  Business: Web, e-commerce, transactions, stocks, …  Science: Remote sensing, bioinformatics, scientific simulation, …  Society and everyone: news, digital cameras, YouTube We are drowning in data, but starving for knowledge!

“Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets April 24, 2020 Data Mining: Concepts and Techniques 6

Evolution of Sciences

     Before 1600, empirical science 1600-1950s, theoretical science  Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding. 1950s-1990s, computational science  Over the last 50 years, most disciplines have grown a third, computational branch (e.g. empirical, theoretical, and computational ecology, or physics, or linguistics.)  Computational Science traditionally meant simulation. It grew out of our inability to find closed-form solutions for complex mathematical models. 1990-now, data science  The flood of data from new scientific instruments and simulations   The ability to economically store and manage petabytes of data online The Internet and computing Grid that makes all these archives universally accessible  Scientific info. management, acquisition, organization, query, and visualization tasks scale almost linearly with data volumes. Data mining is a major new challenge!

Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science , Comm. ACM, 45(11): 50-54, Nov. 2002 April 24, 2020 Data Mining: Concepts and Techniques 7

Evolution of Database Technology

     1960s:  Data collection, database creation, IMS and network DBMS 1970s:  Relational data model, relational DBMS implementation 1980s:   RDBMS, advanced data models (extended-relational, OO, deductive, etc.) Application-oriented DBMS (spatial, scientific, engineering, etc.) 1990s:  Data mining, data warehousing, multimedia databases, and Web databases 2000s    Stream data management and mining Data mining and its applications Web technology (XML, data integration) and global information systems April 24, 2020 Data Mining: Concepts and Techniques 8

Why Not Traditional Data Analysis?

   Tremendous amount of data  Algorithms must be highly scalable to handle such as tera-bytes of data High-dimensionality of data  Micro-array may have tens of thousands of dimensions High complexity of data       Data streams and sensor data Time-series data, temporal data, sequence data Structure data, graphs, social networks and multi-linked data Heterogeneous databases and legacy databases Spatial, spatiotemporal, multimedia, text and Web data Software programs, scientific simulations April 24, 2020 Data Mining: Concepts and Techniques 9

Why Data Mining?—Potential Applications

  Data analysis and decision support  Market analysis and management   Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation Risk analysis and management  Other Applications  Text mining (news group, email, documents) and Web mining  Stream data mining   Forecasting, customer retention, improved underwriting, quality control, competitive analysis Fraud detection and detection of unusual patterns (outliers) Bioinformatics and bio-data analysis April 24, 2020 Data Mining: Concepts and Techniques 10

What Is Data Mining?

 

Data mining (knowledge discovery from data)

 Extraction of interesting ( non-trivial, implicit,  previously unknown and potentially useful) patterns or knowledge from huge amount of data the non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data

(

从 数据集 中

识别出

有效的 、 新

颖的

、 潜在有 用的 ，以及 最

终可理解

的 模式 的 非平凡

过程 )

Alternative names

 Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, etc.

2020 年 4 月 24 日星期五 Data Mining: Concepts and Techniques 11

Knowledge Discovery (KDD) Process

 Data mining—core of knowledge discovery process

Pattern Evaluation Data Mining Task-relevant Data Data Warehouse Data Cleaning Data Integration Selection

April 24, 2020

Databases

Data Mining: Concepts and Techniques 12

KDD Process: Several Key Steps

         Learning the application domain  relevant prior knowledge and goals of application Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation  Find useful features, dimensionality/variable reduction, invariant representation Choosing functions of data mining  summarization, classification, regression, association, clustering Choosing the mining algorithm(s) Data mining : search for patterns of interest Pattern evaluation and knowledge presentation  visualization, transformation, removing redundant patterns, etc.

Use of discovered knowledge April 24, 2020 Data Mining: Concepts and Techniques 13

Data Mining and Business Intelligence

Increasing potential to support business decisions Decision Making Data Presentation

Visualization Techniques

Data Mining

Information Discovery

Data Exploration

Statistical Summary, Querying, and Reporting

April 24, 2020

Data Preprocessing/Integration, Data Warehouses Data Sources

Paper, Files, Web documents, Scientific experiments, Database Systems

Data Mining: Concepts and Techniques

End User Business Analyst Data Analyst DBA

14

Architecture: Typical Data Mining System

April 24, 2020 Graphical User Interface Pattern Evaluation Data Mining Engine Database or Data Warehouse Server

data cleaning, integration, and selection

Knowl edge Base

Database Data Warehouse World-Wide Web Other Info Repositories

Data Mining: Concepts and Techniques 15

Data Mining: On What Kinds of Data?

  Database-oriented data sets and applications  Relational database, data warehouse, transactional database Advanced data sets and advanced applications  Object-relational databases        temporal data, sequence data, Time-series data (incl. bio-sequences) Spatial data （空间数据） and spatiotemporal data （时间空间数据） Text databases ， Multimedia database Heterogeneous （异构） databases and legacy （遗产） databases Data streams and sensor data The World-Wide Web Structure data, graphs, social networks and multi-linked data April 24, 2020 Data Mining: Concepts and Techniques 16

Data Mining Functionalities

   Multidimensional concept description: Characterization and discrimination  Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions Frequent patterns, association, correlation vs. causality  Diaper  Beer [0.5%, 75%] (Correlation or causality?) Classification and prediction  Construct models (functions) that describe and distinguish classes or concepts for future prediction  E.g., classify countries based on (climate), or classify cars based on (gas mileage)  Predict some unknown or missing numerical values April 24, 2020 Data Mining: Concepts and Techniques 17

Data Mining Functionalities (2)

   Cluster analysis   Outlier analysis  Outlier: Data object that does not comply with the general behavior of the data  Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Maximizing intra-class similarity & minimizing interclass similarity Noise or exception? Useful in fraud detection, rare events analysis Trend and evolution analysis  Trend and deviation: e.g., regression analysis    Sequential pattern mining Periodicity analysis Similarity-based analysis April 24, 2020 Data Mining: Concepts and Techniques 18

Are All the “Discovered” Patterns Interesting?

   Data mining may generate thousands of patterns: Not all of them are interesting  Suggested approach: Human-centered, query-based, focused mining

Interestingness measures

 A pattern is interesting if it is easily understood by humans, valid on new or test data with some degree of certainty , potentially useful , novel, or validates some hypothesis that a user seeks to confirm

Objective vs. subjective interestingness measures

  Objective : based on statistics and structures of patterns , e.g., support, confidence, etc.

Subjective : based on user’s belief in the data, e.g., unexpectedness, novelty, actionability, etc.

April 24, 2020 Data Mining: Concepts and Techniques 19

Find All and Only Interesting Patterns?

  Find all the interesting patterns: Completeness  Can a data mining system find all need to find all the interesting patterns? Do we of the interesting patterns?

  Heuristic( 启发式 ) vs. exhaustive （全空间） search Association vs. classification vs. clustering Search for only interesting patterns: An optimization problem  Can a data mining system find only the interesting patterns ?

 Approaches  First general all the patterns and then filter out the uninteresting ones  Generate only the interesting patterns—mining query optimization April 24, 2020 Data Mining: Concepts and Techniques 20

Classification of data mining systems

Database Technology Statistics Machine Learning

Data Mining

Visualization Pattern Recognition April 24, 2020 Algorithm Other Disciplines

Data Mining: Confluence of Multiple Disciplines

Data Mining: Concepts and Techniques 21

Multi-Dimensional View of Data Mining

   

Data to be mined

 Relational, data warehouse, transactional, stream, object oriented/relational, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW

Knowledge to be mined

  Multiple/integrated functions and mining at multiple levels

Techniques utilized

 Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc.

Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, etc.

Applications adapted

 Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc.

April 24, 2020 Data Mining: Concepts and Techniques 22

Data Mining: Classification Schemes

  General functionality  Descriptive data mining  Predictive data mining Different views lead to different classifications  Data view: Kinds of data to be mined  Knowledge view: Kinds of knowledge to be discovered  Method view: Kinds of techniques utilized  Application view: Kinds of applications adapted April 24, 2020 Data Mining: Concepts and Techniques 23

Integration of Data Mining and Data Warehousing

    No coupling—flat file processing, not recommended Loose coupling  Fetching data from DB/DW Semi-tight coupling—enhanced DM performance  Provide efficient implement a few data mining primitives in a DB/DW system, e.g., sorting, indexing, aggregation, histogram analysis, multiway join, precomputation of some stat functions Tight coupling—A uniform information processing environment  DM is smoothly integrated into a DB/DW system, mining query is optimized based on mining query, indexing, query processing methods, etc.

April 24, 2020 Data Mining: Concepts and Techniques 24

Major Issues in Data Mining

   Mining methodology      Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web Performance: efficiency （效率） , effectiveness （效果） , and scalability Pattern evaluation: the interestingness problem Incorporation of background knowledge Handling noise and incomplete data  Parallel, distributed and incremental mining methods  Integration of the discovered knowledge with existing one: knowledge fusion User interaction    Data mining query languages Expression and visualization of data mining results Interactive mining of knowledge at multiple levels of abstraction Applications and social impacts  Domain-specific data mining  Protection of data security, integrity, and privacy April 24, 2020 Data Mining: Concepts and Techniques 25

Summary

       Data mining: Discovering interesting patterns from large amounts of data A natural evolution of database technology, in great demand, with wide applications A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation Mining can be performed in a variety of information repositories Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc.

Data mining systems and architectures Major issues in data mining April 24, 2020 Data Mining: Concepts and Techniques 26

April 24, 2020 Data Mining: Concepts and Techniques 27

Top-10 Most Popular DM Algorithms: 18 Identified Candidates (I)

  Classification    #1. C4.5: Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann., 1993.

#2. CART: L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, 1984.

#3. K Nearest Neighbours (kNN): Hastie, T. and Tibshirani, R. 1996. Discriminant Adaptive Nearest Neighbor Classification. TPAMI. 18(6)  #4. Naive Bayes Hand, D.J., Yu, K., 2001. Idiot's Bayes: Not So Stupid After All? Internat. Statist. Rev. 69, 385-398.

Statistical Learning （统计学习）     #5. SVM: Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag.

#6. EM: McLachlan, G. and Peel, D. (2000). Finite Mixture Models. J. Wiley, New York. Association Analysis #7. Apriori: Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In VLDB '94.

#8. FP-Tree: Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without candidate generation. In SIGMOD '00.

April 24, 2020 Data Mining: Concepts and Techniques 28

The 18 Identified Candidates (II)

   Link Mining （连接挖掘）  #9. PageRank: Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine. In WWW-7, 1998.

 #10. HITS: Kleinberg, J. M. 1998. Authoritative sources in a hyperlinked environment. SODA, 1998.

Clustering   #11. K-Means: MacQueen, J. B., Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967.

#12. BIRCH: Zhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficient data clustering method for very large databases. In SIGMOD '96.

Bagging （装袋）  and Boosting （提升） #13. AdaBoost: Freund, Y. and Schapire, R. E. 1997. A decision theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119-139.

April 24, 2020 Data Mining: Concepts and Techniques 29

The 18 Identified Candidates (III)

    Sequential Patterns  #14. GSP: Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns: Generalizations and Performance Improvements. In Proceedings of the 5th International Conference on Extending Database Technology, 1996.

 #15. PrefixSpan: J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In ICDE '01.

Integrated Mining  #16. CBA: Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and association rule mining. KDD-98. Rough Sets  #17. Finding reduct: Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992 Graph Mining  #18. gSpan: Yan, X. and Han, J. 2002. gSpan: Graph-Based Substructure Pattern Mining. In ICDM '02.

April 24, 2020 Data Mining: Concepts and Techniques 30

Top-10 Algorithm Finally Selected at ICDM’06

         

#1: C4.5 (61 votes) #2: K-Means (60 votes) #3: SVM (58 votes) #4: Apriori (52 votes) #5: EM (48 votes) #6: PageRank (46 votes) #7: AdaBoost (45 votes) #7: kNN (45 votes) #7: Naive Bayes (45 votes)

（朴素贝叶斯）

#10: CART (34 votes)

（ 树 ） Classification And Regression Trees 分类回归 April 24, 2020 Data Mining: Concepts and Techniques 31

A Brief History of Data Mining Society

      1989 IJCAI Workshop on Knowledge Discovery in Databases  Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991) 1991-1994 Workshops on Knowledge Discovery in Databases  1995-1998 International Conferences on Knowledge Discovery in Databases and Data Mining (KDD’95-98)  Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996) Journal of Data Mining and Knowledge Discovery (1997) ACM SIGKDD conferences since 1998 and SIGKDD Explorations More conferences on data mining  PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc.

ACM Transactions on KDD starting in 2007 April 24, 2020 Data Mining: Concepts and Techniques 32

Conferences and Journals on Data Mining

 KDD Conferences       ACM SIGKDD Int. Conf. on Knowledge Discovery in Databases and Data Mining ( KDD ) SIAM Data Mining Conf. ( SDM ) (IEEE) Int. Conf. on Data Mining ( ICDM ) Conf. on Principles and practices of Knowledge Discovery and Data Mining ( PKDD ) Pacific-Asia Conf. on Knowledge Discovery and Data Mining ( PAKDD )  Other related conferences      ACM SIGMOD VLDB (IEEE) ICDE WWW, SIGIR ICML, CVPR, NIPS Journals     Data Mining and Knowledge Discovery (DAMI or DMKD) IEEE Trans. On Knowledge and Data Eng. (TKDE) KDD Explorations ACM Trans. on KDD April 24, 2020 Data Mining: Concepts and Techniques 33

Where to Find References? DBLP, CiteSeer, Google

      Data mining and KDD (SIGKDD: CDROM)   Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.

Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD Database systems (SIGMOD: ACM SIGMOD Anthology — CD ROM)  Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA  Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc.

AI & Machine Learning   Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc.

Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE-PAMI, etc.

Web and IR  Conferences: SIGIR, WWW, CIKM, etc.

 Journals: WWW: Internet and Web Information Systems, Statistics  Conferences: Joint Stat. Meeting, etc.

 Journals: Annals of statistics, etc.

Visualization   Conference proceedings: CHI, ACM-SIGGraph, etc.

Journals: IEEE Trans. visualization and computer graphics, etc.

April 24, 2020 Data Mining: Concepts and Techniques 34

Recommended Reference Books

             

S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan Kaufmann, 2002 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000 T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003 U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996 U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001 J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2 nd ed., 2006 D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001 T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001 B. Liu, Web Data Mining, Springer 2006.

T. M. Mitchell, Machine Learning, McGraw Hill, 1997 G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991 P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005 S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998 I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2 nd ed. 2005

April 24, 2020 Data Mining: Concepts and Techniques 35